import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cv2
import math
from tensorflow.keras import models, layers
from tensorflow.keras.optimizers import RMSprop, Adam
from google.colab import drive
drive.mount('/content/drive')
1. Import the libraries, load dataset, print shape of data, visualize the images in train and test set
# Load train & test images files
TrainImages = np.load('/content/drive/MyDrive/COVID-19 Project/trainimage.npy')
TestImages = np.load('/content/drive/MyDrive/COVID-19 Project/testimage.npy')
# Load train & test labels files
TrainLabels = pd.read_csv('/content/drive/MyDrive/COVID-19 Project/trainLabels.csv')
TestLabels = pd.read_csv('/content/drive/MyDrive/COVID-19 Project/testLabels.csv')
print('Train Images shape: ',TrainImages.shape)
print('Test Images shape: ',TestImages.shape)
print('Train Labels shape: ',TrainLabels.shape)
print('Test Labels shape: ',TestLabels.shape)
TrainLabels.iloc[0]
# visualize the images in train set
plt.figure(figsize=(20,200))
for i in range(250):
plt.subplot(50, 5, i + 1)
plt.imshow(TrainImages[i])
plt.title(TrainLabels.iloc[i].Label)